The Measurement and Methods Core (Core D) of the Native Elder Research Center (NERC) is broadly focused to accommodate the diverse research portfolio of the NERC and the Native Investigators (NIs) participating in the Investigator Development Core. This Core has 2 major responsibilities. The first is to identify, assess, and develop measurement tools and methodologies to capture and analyze the effects of ethnocultural variability on the health of the American Indian/Alaska Native (AI/AN) elderly population. The second is to support the NIS in the: 1) conduct of their secondary data analysis projects; 2) development and implementation of their pilot studies and 3) crafting of their R01-like research grant applications. This Core links a social science perspective on operationalizing key ethnocultural constructs with epidemiological and biostatistical tools of data analysis. An overarching principle is to expose the NIs to measurement tools and methods that are valid and reliable in distinct cultural contexts, especially as they pertain to our organizing theme - quality of care as it relates to health disparities. The Measurement and Methods Core will be directed by Jack Goldberg, Ph.D., a NERC Core Faculty member and a Research Professor of Epidemiology at the University of Washington in the Department of Epidemiology. Dr. Goldberg is a senior methodologist whose academic career has focused on teaching and mentoring students in the quantitative approaches used in public health and biomedical research. Thus, the specific aims of this Core are to: 1) develop strategies for assessing cultural variability in measures and to better understand the impact of culture on health; 2) assess the performance of measurement tools across different ethnocultural settings for consistency, reliability and validity; 3) provide training and mentoring to the NIs in the application of methodologies to specific research questions; 4) construct an annotated catalogue of data sets containing information on the health of AI/ANs that can used by the NIs; and 5) increase the understanding of unique data collection and human subject protection issues that are critical to conducting research in AI/AN communities, particularly as they pertain to study design, implementation, and measurement issues.